Comparison of discovery rates and prognostic utility of [68Ga]Ga-PSMA-11 PET/CT and circulating tumor DNA in prostate cancer—a cross-sectional study

Background Circulating-tumor DNA (ctDNA) and prostate-specific membrane antigen (PSMA) ligand positron-emission tomography (PET) enable minimal-invasive prostate cancer (PCa) detection and survival prognostication. The present study aims to compare their tumor discovery abilities and prognostic values. Methods One hundred thirty men with confirmed PCa (70.5 ± 8.0 years) who underwent [68Ga]Ga-PSMA-11 PET/CT (184.8 ± 19.7 MBq) imaging and plasma sample collection (March 2019–August 2021) were included. Plasma-extracted cell-free DNA was subjected to whole-genome-based ctDNA analysis. PSMA-positive tumor lesions were delineated and their quantitative parameters extracted. ctDNA and PSMA PET/CT discovery rates were compared, and the prognostic value for overall survival (OS) was evaluated. Results PSMA PET discovery rates according to castration status and PSA ranges did differ significantly (P = 0.013, P < 0.001), while ctDNA discovery rates did not (P = 0.311, P = 0.123). ctDNA discovery rates differed between localized and metastatic disease (P = 0.013). Correlations between ctDNA concentrations and PSMA-positive tumor volume (PSMA-TV) were significant in all (r = 0.42, P < 0.001) and castration-resistant (r = 0.65, P < 0.001), however not in hormone-sensitive patients (r = 0.15, P = 0.249). PSMA-TV and ctDNA levels were associated with survival outcomes in the Logrank (P < 0.0001, P < 0.0001) and multivariate Cox regression analysis (P = 0.0023, P < 0.0001). Conclusion These findings suggest that PSMA PET imaging outperforms ctDNA analysis in detecting prostate cancer across the whole spectrum of disease, while both modalities are independently highly prognostic for survival outcomes. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s00259-024-06698-7.


Study design
In this retrospective single-centre study with prospective sample collection conducted at the Medical University of Vienna (March 2019 -August 2021), 187 men with confirmed PCa referred for [ Ga]Ga-PSMA-11 PET/CT underwent PET/CT imaging and blood sample ⁶⁸ collection.An all-comer recruitment strategy was employed.All patients gave their written informed consent for imaging, blood sample collection and associated analysis.This study was approved by the ethics committee of the Medical University of Vienna (ID: 1649/2016).
For this analysis, patients with histologically proven PCA, known PSA levels and castration status were included, while patients with active or a history of concomitant malignancies other than PCA (N = 11), unknown PSA values (N = 31) and unknown castration status (N = 15) were excluded.(Figure 1) Clinical data, such as PSA levels, castration status and pre-, concurrent and post-imaging therapy data, were gleaned retrospectively from the medical records.hsPC was defined as PCa, which was not subjected to prior antiandrogens or did not advance in an castration environment.CRPC as PCa which progressed despite antiandrogen treatment in a castration environment [1].
Follow-up and overall survival (OS) data (censorization 13th August 2023) were sourced from Statistic Austria, the national health statistical service.The primary endpoints of this study were a) ctDNA and PSMA PET/CT discovery rates according to PSA levels, b) the relationship of ctDNA concentrations and the PSMA-TV in all patients and according to their respective castration status and c) the prognostic value of ctDNA and PSMA-TV levels with regard to overall survival (OS).

Plasma sample collection and storage
Prior to tracer injection, blood samples were collected in Cell-Free DNA BCT tubes (Streck Inc., Nebraska, USA).Then, the collected samples were centrifuged twice (1. round: 2000g for 20 min., 2. round: 3200 g for 30 min.) to completely remove of any cellular debris.
Afterwards, the derived plasma was stored at -80 °C till further processing.

Imaging protocol
Scans were obtained on a Biograph TruePoint PET/CT scanner (Siemens Healthineers, Erlangen, Germany).The patients received an intravenous injection averaging 184.8 MBq (± 19.7 SD) of [ Ga]Ga-PSMA-11.Static whole-body scans were performed from the skull ⁶⁸ base to the upper thigh one hour after the tracer injection.CT scans were acquired first at 120 kV and 230 mAs with intravenous contrast (CT matrix size 512x512), except contraindications for contrast application existed.Afterwards, PET scans were acquired in 3-4 bed positions with a matrix size of 168x168, followed by iterative reconstruction using a point-spread-function-based algorithm.

Image analysis
Two nuclear medicine physicians interpreted the images on a dedicated workstation using the Hybrid 3D software (version 4.0.0,Hermes Medical Solutions, Stockholm, Sweden).All primary and secondary PSMA-expressing tumour lesions from the skull to the upper femur were manually delineated and labelled per their anatomical location (prostate, lymph nodes, bone, organ).Lesion identification was performed qualitatively, informed by liver uptake, followed by semiautomatic delineation using a region-growing algorithm (Hybrid 3D software, version 4.0.0).The PSMA-TV, standardized uptake values (SUV) were extracted from an aggregated master lesion, comprised of all delineated lesions, as well as per anatomical region.The anatomic tumor region which contributed most to the overall PSMA-TV was defined as the dominant tumor fraction.

DNA extraction, quantification
The cfDNA was extracted from the stored plasma using the QIAamp Circulating Nucleic Acid Kit (QIAGEN, Venlo, Netherlands) according to the manufacturer's instructions from 4 mL of plasma.Subsequently, the extracted cfDNA was stored at -20°C until further analysis.
The cfDNA was quantified on the Fragment Analyzer (Agilent, California, USA) system using the HS NGS Fragment Kit (Agilent, California, USA), according to the manufacturer's instructions.Electropherograms were read and analysed using the PROSize software (version 2.0, Agilent, California, USA), enabling the calculation of areas under the curves proportional to the DNA concentration of each sample, thereby quantifying the cfDNA and proportion of size fractions in the sample.

Bioinformatic analysis
We developed an in-house method to analyze the ctDNA fraction in blood samples using low-coverage WGS sequencing.Raw sequencing reads were initially mapped to the human genomic reference GRCh38 using the BWA tool [2].After mapping the raw sequencing reads to a reference genome, they were counted in 500kb bin intervals.These bin counts underwent normalization based on sample size and GC content to address biases and variations.From this data, we determined an initial, approximate ctDNA fraction using the density plots of the bin sizes.To call CNVs, we employed an algorithm using the normalized binned read counts, incorporating a negative binomial distribution for individual bin counts to handle overdispersion similar to the ichorCNA methodology [3].This was followed by using a dynamic Bayesian network model for holistic CNV predictions.The procedure was iterative, with CNVs re-called based on the updated ctDNA fraction and the ctDNA fractions recalculated using the new CNV predictions.In cases where no CNVs were discerned, we assigned the ctDNA fraction a default value of 0.05 and repeated the CNV calling.We measured the quality of our modelled CNVs and ctDNA by examining the residual difference between the actual bin sizes and the sizes predicted post-CNV and ctDNA adjustments.To test the significance of our predicted ctDNA for each sample, we compared residuals from our primary model to those from a noise model created using the same bin count data but with randomly permuted bins, employing the Kolmogorov-Smirnov Test for this purpose.Our final results excluded samples without called CNVs, those with a Kolmogorov-Smirnov Test p-value greater than 0.05, and samples predominantly predicting deletions around chromosome centromeres -a potential sign of an unidentified technical bias.

Statistical analysis
Continuous variables are expressed as mean (± standard deviation (SD)), while categorical outcomes are expressed as absolute and relative (%) frequencies.
The association of the ctDNA and PSMA PET tumor signal discovery rates and PSMA PET dominant fraction with castration status, PSA ranges, and disease extent were assessed using the Chi-squared and Fisher's tests, based on the underlying contingency table data distribution.
For the comparison of non-normalized and PSMA-TV normalized ctDNA concentrations according to PSMA PET disease extent and dominant lesion fraction respectively, normality and heteroskedasticity were evaluated using the Shapiro-Wilk and Levene's test, respectively, followed by difference testing using the Kruskal-Wallis test.In case the null hypothesis was rejected, post-hoc adjusted pairwise analysis was performed with Dunn-Bonferoni's test.
The normality of ctDNA concentration and PSMA-TV was evaluated with the Shapiro-Wilk test.Spearman's coefficient was used to assess the correlation between ctDNA concentrations and PSMA-TV, judging correlations as very strong from 1 to 0.9, strong from 0.9 to 0.7, moderate from 0.7 to 0.5, low from 0.5 to 0.3 and weak from 0.3 to 0.
Receiver-operating-characteristic curves were used to assess PSMA-TV's ability to predict ctDNA discovery in all and metastatic patients, expressed as area under the curves (AUCs) and 95% confidence intervals (CI).
OS probabilities and their pointwise 95% CI from the date of inclusion till death were estimated using the Kaplan-Meier method.Survival distributions between high and low ctDNA and PSMA-TV groups (cutoff respective median values) as well as between the cross-validated machine learning classified groups were compared using the non-parametric Logrank test.
To evaluate the relationship between OS and the binary explanatory variables ctDNA concentration and PSMA-TV a multivariate Cox regression analysis was performed after checking data for multicollinearity and proportional hazards with the Belsley-Kuh-Welsch technique and Schoenfeld residuals, respectively.
The alpha risk was set for all statistical analyses to 5% (α = 0.05).All CIs are 95% CI.
Statistical analysis was performed with the EasyMedStat software (version 3.24, EasyMedStat, Paris, France).

Machine learning workflow
To explore potential non-linear relationships between imaging and plasma-derived variables and 1-year OS, a 100-fold Monte Carlo (MC) cross-validated machine learning scheme was employed.To evaluate the incremental predictive value of combining ctDNA analysis and PSMA PET, a compound modelling scheme, incorporating imaging and plasma-derived markers, as well as an imaging-only and plasma-only modelling scheme were trained and their performances compared.All features used in the respective ML schemes and their associated definitions are summarized in the supplemental Appendix.
In order to enable robust machine learning performance estimations, 100-fold Monte Carlo (MC) cross-validation schemes using a 80% to 20% training-to-test set ratio were employed.
Modelling was strictly conducted on a training data subset.The model performances were exclusively evaluated on the test data.
For each fold, fold-wise preprocessing using feature standardization, k-nearest neighbour feature imputation, minimum-redundancy-maximum-relevance-based (mRMR) [4] feature selection and class balancing using synthetic minority over-sampling technique (SMOTE) [5] was employed.An automated hyperparameter optimization via random search was used.
Subsequently, six different machine learning (ML) classifiers were trained on the preprocessed training data, namely decision trees (DT), logistic regression (LGR), k-nearest neighbours (kNN), random forest (RF), extreme gradient boosting (XBG) and explainable boosting machine (EBM) [6].Model prediction performance was estimated via the MC crossvalidation scheme using confusion matrix analytics.True positive, true negative, false positive and false negative confusion matrix entries were calculated by evaluating the validation samples in each fold.Sensitivity (SNS), specificity (SPC), accuracy (ACC), positive predictive values (PPV), negative predictive values (NPV), balanced accuracy (BACC) and area under the receiver operating characteristic (AUC) were used as performance metrics.

Results
ctDNA and PSMA PET discovery rate differences according to castration status The

Compound model performances and feature importances
The best-performing compound classifier for the predicted endpoint 1yOS was RF.
For the individual performances of all compound classifiers see Table 2.For the most important features according to SHAP analysis of the best-performing model (RF) see Figure 2.For the Kaplan-Meier curves visualizing the survival probability stratification of the bestperforming imaging-based classifier see Figure 3.

Imaging model performances and feature importances
The best-performing imaging-based classifier for the predicted endpoint 1yOS was RF.
For the individual performances of all imaging-based classifiers see Table 3.For the most important features according to SHAP analysis of the best-performing model (RF) see Figure 4.For the Kaplan-Meier curves visualizing the survival probability stratification of the bestperforming imaging-based classifier see Figure 5.

Plasma model performance and feature importance
The best-performing plasma-based classifier for the predicted endpoint 1yOS was XGB.
For the individual performances of all plasma-based classifiers see Table 4.For the most important features according to SHAP analysis of the best-performing model (XGB) see Figure 6.For the Kaplan-Meier curves visualizing the survival probability stratification of the best-performing plasma-based classifier see Figure 7.

Figure 1 :
Figure 1: ctDNA and PSMA PET discovery rates according to castration status

Figure 2 :
Figure 2: SHAP analysis plot illustrating the contribution and ranking of the ten most important input features to best-performing imaging model

Figure 4 :
Figure 4: SHAP analysis plot illustrating the contribution and ranking of the ten most important input features to best-performing imaging model

Figure 6 :
Figure 6: SHAP analysis plot illustrating the contribution and ranking of the ten most important input features to best-performing plasma model
Clinical and demographic table of patients used in 1-year OS predictionA total of 105 patients (age 71.5 ± 7.74 years) were eligible for the 1yOS survival prediction based on the available follow-up and outcome data.Their clinical and demographic characteristics are presented in Table1.For input features used in machine learning models see supplementary

Table 1 .
Demographic and clinical patient data of 1yOS survival prediction cohortQualitative data as numbers and percentages; Continuous data as mean, standard deviation and range; Local disease comprised of prostate and seminal vesicle lesions

Table 2 .
Cross-validated machine learning performance metrics of the compound models predicting 1-year overall survival.

Table 3 .
Cross-validated machine learning performance metrics of the imaging-based models predicting 1-year overall survival.

Table 4 .
Cross-validated machine learning performance metrics of the plasma-based models predicting 1-year overall survival.

Table 5 .
Cross-validated machine learning performance metrics of the best-performing models trained on

Table 6 .
Input features used in the compound, imaging and plasma-based modelsNew York, NY, USA: Association for Computing Machinery; 2016.p. 785-94.